JFrog ML: Unifying DevOps, DevSecOps and MLOps in one platform

🚀 DevOps News: October 2025 Highlights Knative has graduated at the CNCF, making serverless applications on Kubernetes easier and more accessible for everyone. AI-driven DevOps workflows are transforming how teams manage CI/CD, infrastructure, and toolchains—with real-time insights and automation at the forefront. Security is center-stage in DevSecOps: automated compliance and vulnerability detection are now built directly into development pipelines. Kubernetes v1.34 brings new pod-level resource features, and the latest HAProxy patch (CVE-2025-59303) addresses a crucial security issue—keep your clusters up-to-date! DevOps is advancing fast—automation, AI, and security are leading the way.   Which trend excites you most this month? Let's connect and discuss! #DevOps #TechNews #Kubernetes #Serverless #AI #DevSecOps #CloudNative #Automation

Observability 2.0: The New Technology Stack? The days of simply collecting Metrics, Logs, and Traces are over. The latest wave of observability technology is all about Intelligent, Low-Overhead, and Open-Standard insights. Here are the top 3 technologies defining the next era of SRE and DevOps: Agentic AI & Predictive Observability: AI is moving from simple anomaly detection to complex, autonomous agents that can predict system failure, perform automatic root cause analysis (RCA), and even suggest or execute remediation actions. MTTR is becoming MTTP (Mean Time to Prevent). eBPF for Zero-Code Visibility: Extended Berkeley Packet Filter (eBPF) is revolutionizing instrumentation. It allows for ultra-low-overhead, kernel-level visibility (like Continuous Profiling) without changing a single line of application code or requiring new libraries. It's especially powerful for difficult languages like Go and Rust. OpenTelemetry (OTel) + Profiling: OTel is no longer just for Metrics/Logs/Traces. The new focus is on full standardization, including Profiling as a core signal. This drives a powerful narrative: maximum visibility with minimum vendor lock-in. If your observability platform isn't embracing these three, you're missing out on the next level of operational efficiency. Which of these is having the biggest impact on your team right now? #Observability #AIOps #eBPF #OpenTelemetry #DevOps #SRE

Observability Isn’t Optional - It’s the Backbone of Scalable Systems In a world of microservices, serverless functions, and distributed APIs, “monitoring” alone won’t cut it. True observability lets you ask why things fail, not just that they did. To build resilient systems: * Correlate logs, traces, and metrics across services * Implement context propagation (trace IDs, correlation IDs) * Set business-aligned SLOs and alerts, not just infrastructure thresholds * Filter and sample intelligently so your telemetry is actionable, not noise The teams that turn failure into insight win long-term. 👉 Want to build a telemetry framework that scales with your stack? Book a quick huddle and let's chat : https://lnkd.in/dbxaBfFZ #Observability #Microservices #DevOps #SystemArchitecture #SoftwareEngineering

Stop adding dashboards to outage fatigue, add agents that read your estate. Plural’s pitch lands on a crucial shift: DevOps value won’t come from “AI that types YAML,” but from AI that executes 𝑔𝑜𝑣𝑒𝑟𝑛𝑒𝑑 workflows. By wiring autonomous upgrade assistants, RAG-powered troubleshooting, and natural-language infra queries into a GitOps backbone, the platform treats change as code, with diffs, PRs, and explicit approvals as built-in guardrails. A resource graph plus semantic indexing lets agents reason across Kubernetes, IaC, and runtime signals to propose fixes and open PRs that preserve traceability. This is where AI belongs in ops, inside auditable loops. Humans set intent (“double the production DB”), agents assemble the least-risk plan, and GitOps enforces blast-radius control, review, and rollback. Reliability improves not by adding more dashboards, but by compressing MTTR with explainable actions and reversible commits. Treat your platform as a queryable knowledge base and your pipelines as the control surface. With GitOps as the safety harness, AI can remove toil while increasing accountability. More details: 🔨https://www.plural.sh/

What if every smart contract deployment could self‑debug, deploy, and roll back without human touch? The future of Web3 Ops is already here, and it’s rewriting the DevOps playbook. By embedding autonomous agents that learn from chain telemetry, we’re moving from reactive patching to predictive self‑healing, cutting deployment cycles from days to minutes. This not only slashes ops costs but frees engineers to focus on value‑adding features. It also forces governance models to evolve, as decentralized AI must audit itself against regulatory constraints. If you’re still waiting for a human‑centric deployment pipeline, ask yourself: how will your organization stay competitive when the next‑gen ops stack is already optimizing itself? Share one way you plan to pilot autonomous Web3 DevOps. #AutonomousDevOps #Web3Ops #AIForWeb3 #DecentralizedAI #HyperAutomation #FutureOfDeployments #AgenticAI #EthicalWeb3

Agentic AI isn’t just the next buzzword it’s the next operating model. Just as APIs redefined connectivity and microservices reshaped architectures, agents are now emerging as first-class workloads. Enterprises that operationalize them with rigor will shape the next wave of transformation. Here are the 3 trends to watch: 1️⃣ From Pilots to Production Most AI projects stall at the pilot stage. The leaders will operationalize agents with governance, observability, and scale, closing the trust gap that derails most initiatives. 2️⃣ The Rise of AgentOps We’ve moved beyond “prompt engineering.” The future is AgentOps—registries, lifecycle management, policy controls, and monitoring frameworks that turn agents from experimental code into production assets. 3️⃣ Ecosystem & Standards First Shared protocols like MCP and contributions like agent gateways to the Linux Foundation show us where the momentum is. Open ecosystems not closed stacks will drive long-term adoption. 💡 The real question isn’t if agents become part of your architecture, it’s when. The differentiator will be whether your team applies the same rigor to agents that you already do to APIs and microservices. 🔎 What blockers have you faced bringing AI agents from proof-of-concept into production? #AgenticAI #AIOps #Kubernetes #FutureOfWork #AITrends

AI is accelerating code—but new friction is creeping in downstream. DevOps teams are now grappling with quality validation, integration debt, and tangled outputs. 🔗 https://bit.ly/4nzHihe #AIinDevOps #TechDebt

𝐖𝐡𝐚𝐭 𝐢𝐟 𝐲𝐨𝐮𝐫 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐜𝐨𝐮𝐥𝐝 𝐩𝐫𝐞𝐝𝐢𝐜𝐭 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞𝐲 𝐡𝐚𝐩𝐩𝐞𝐧? CI/CD pipelines aren’t just about speed. They’re about intelligence, foresight, and resilience. That’s where AIOps comes in: → Predict problems before they happen with intelligent anomaly detection. → Resolve incidents automatically and reduce human intervention. → Optimize resources dynamically to cut costs and boost performance. → Enhance testing & quality assurance by prioritizing the right checks. → Deploy smarter with AI-driven insights that reduce risk. AIOps transforms your pipeline from a reactive system into a self-aware, adaptive engine, freeing your team to focus on innovation rather than firefighting. Ready to make your DevOps smarter, safer, and more efficient? Swipe through to see 5 ways AIOps is reshaping CI/CD in 2025. #DevOps #AIOps #CICD #CloudComputing #Automation #AIinTech #Innovation

AI is accelerating code—but new friction is creeping in downstream. DevOps teams are now grappling with quality validation, integration debt, and tangled outputs. 🔗 https://bit.ly/473DUEu #AIinDevOps #TechDebt